Pitfalls in Analyzing Diffusion Data

Before anyone undertakes research where diffusion weighted MR imaging will be done, I think he or she should read a 2010 article by Derek K. Jones and Mara¬†Cercignani. This article – Twenty-five pitfalls in the analysis of diffusion MRI data – was published in NMR in Biomedicine. Here’s the abstract:

Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI.

I think this is a must-read article for anyone working with diffusion imaging. There are a lot of wonderful things that we can do with diffusion weighted images but we have to be conscious of the limitations and sources of bias in this type of imaging. The day we stop questioning and challenging our methods and results – thinking critically about our methods and personal biases – is the day our science really suffers. We as scientists have an obligation to do the best work possible because our research can have broad, applied implications that might affect a lot of people for good or ill. At the very least, scientists add to the general pool of knowledge and we want that pool as free from contaminants as possible. It is critical thinkers like Jones and Cercignani who help keep scientists honest with their (our) methods, even if we sometimes act out of innocence by using methods we do not really understand.

Here’s the reference: Jones and Cercignani. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR in biomedicine (2010) vol. 23 (7) pp. 803-820.

About Jared Tanner

I have a PhD in Clinical and Health Psychology with an emphasis in neuropsychology at the University of Florida. I previously studied at Brigham Young University. I am currently a Research Assistant Professor at the University of Florida. I spend the bulk of my research time dealing with structural magnetic resonance images of the brain. My specialty is with traditional structural MR images, such as T1-weighted and T2-weighted images, as well as diffusion weighted images. I also look at the cognitive and behavioral functioning of individuals with PD and older adults undergoing orthopedic surgery. Funding for the images came from NINDS K23NS060660 (awarded to Catherine Price, University of Florida). Brain images may not be used without my written permission (grant and software requirements).